A fuzzy Kalman filter algorithm is developed for target tracking applications and its performance evaluated using several numerical examples. The approach is relatively novel. A comparison with Kalman filter and an adaptive tuning algorithm is carried out. The applicability and usefulness of fuzzy logic in data fusion is also demonstrated. The performance of both the extended Kalman filter and ...

One method to account for parameters errors in the Kalman filter is to ‘consider’ their effect in the so-called Schmidt-Kalman filter. This paper addresses issues that arise when implementing a consider Kalman filter as a real-time, recursive algorithm. A favorite implementation of the Kalman filter as an onboard navigation subsystem is the UDU formulation. A new way to implement a UDU Schmidt-...

The Kalman filter is the optimal minimum-variance state estimator for linear dynamic systems with Gaussian noise. In addition, the Kalman filter is the optimal linear state estimator for linear dynamic systems with non-Gaussian noise. For nonlinear systems various modifications of the Kalman filter (e.g., the extended Kalman filter, the unscented Kalman filter, and the particle filter) have bee...

The Kalman filter is the minimum-variance state estimator for linear dynamic systems with Gaussian noise. Even if the noise is non-Gaussian, the Kalman filter is best linear estimator. For nonlinear systems it is not possible, in general, to derive the optimal state estimator in closed form, but various modifications of the Kalman filter can be used to estimate the state. These modifications in...

The Kalman filter is used in this paper as a framework for space time data analysis. Using Kalman filtering it is possible to include physically based simulation models into the data analysis procedure. Attention is concentrated on the development of fast filter algorithms to make Kalman filtering feasible for high dimensional space time models. The ensemble Kalman filter and the reduced rank s...

We present a system for detecting and tracking the lanes of high-speed roads, in order to warn the driver of accidental lane departures. The proposed method introduces a novel variant of the classic Hough transform, better equipped to detect and locate linear road markings with a common vanishing point. This is combined with a simple model of the lane and an Extended Kalman Filter to make detec...

This paper conducts a performance evaluation for the ultra‐tight integration of a Global positioning system (GPS) and an inertial navigation system (INS), using nonlinear filtering approaches with an interacting multiple model (IMM) algorithm. An ultra‐tight GPS/INS architecture involves the integration of in‐phase and quadrature components from the correlator of...